Based on the surveys and the statistic data during 1980-2003, the variation character of grain yield per unit area in Northeast China and its main factors have been discussed by the methods of statistics and grey corr...Based on the surveys and the statistic data during 1980-2003, the variation character of grain yield per unit area in Northeast China and its main factors have been discussed by the methods of statistics and grey correlation analysis. The results show that: 1) the grain yield per unit area has been taking on an increasing trend in the recent 20 years. It increased from 2519.80kg/ha in 1980 to 4216.11kg/ha in 2003, with an increasing rate of 67.32%; 2) the variation of grain yield per unit area is considerably prominent and its range is also very great, with the maximal increase rate of 42.59% and maximal decrease rate of 21.13%, respectively, which are far above the whole Chinese average level; 3) the variation of main crops' yield per unit area is remarkable, which takes on the character that the yield of corn is much higher than that of soybean and rice; and 4) the grey correlation analysis shows that the most important factors impacting the variation of grain yield per unit area are the total power of agricultural machinery, the consumption of chemical fertilizer and effective irrigated area. However, the influence of natural disaster and income level should not be ignored. Effective ways to improve grain yield per unit area are to construct farmland improvement groundwork, reclaim the middle- and low-yield farmland, etc.展开更多
In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor me...In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.展开更多
基金Under the auspices of the National Natural Science Foundation of China (No. 40601027)
文摘Based on the surveys and the statistic data during 1980-2003, the variation character of grain yield per unit area in Northeast China and its main factors have been discussed by the methods of statistics and grey correlation analysis. The results show that: 1) the grain yield per unit area has been taking on an increasing trend in the recent 20 years. It increased from 2519.80kg/ha in 1980 to 4216.11kg/ha in 2003, with an increasing rate of 67.32%; 2) the variation of grain yield per unit area is considerably prominent and its range is also very great, with the maximal increase rate of 42.59% and maximal decrease rate of 21.13%, respectively, which are far above the whole Chinese average level; 3) the variation of main crops' yield per unit area is remarkable, which takes on the character that the yield of corn is much higher than that of soybean and rice; and 4) the grey correlation analysis shows that the most important factors impacting the variation of grain yield per unit area are the total power of agricultural machinery, the consumption of chemical fertilizer and effective irrigated area. However, the influence of natural disaster and income level should not be ignored. Effective ways to improve grain yield per unit area are to construct farmland improvement groundwork, reclaim the middle- and low-yield farmland, etc.
文摘In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.
